如题“MONTE CARLO METHODS FOR ELECTROMAGNETICS”

The SAGE Handbook of Online Research Methods(2nd) 英文无水印原版pdf 第2版 pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

内点法讲义 (ORIE 6300 Lecture Notes)

Robert M. Freund and Jorge Vera

Simultaneous Localization and Mapping for Mobile Robots Introduction and Methods 2012

本书主要论述如下四个问题：1.Compressive Sensing and Structured Random Matrices; 2.Numerical Methods for Sparse Recovery; 3.Sparse Recovery in Inverse Problems; 4.An Introduction to Total Variation for Image Analysis.

Statistical and Econometric Methods for Transportation Data Analysis

逆问题是几乎所有遥感探测的数学原理，诸如医学成像、地震探测、雷达成像，超声探测等。掌握了逆问题求解方法，也就掌握了不同探测模式的共同本质。

Mathematical Methods for Physicists by G.Arfken.pdf sixth edition

Introducing Monte Carlo Methods with R Introducing Monte Carlo Methods with R (Use R) By Christian P. Robert, George Casella Publisher: Springer Number Of Pages: 302 Publication Date: 2009-12-14 ISBN-10 / ASIN: 1441915753 ISBN-13 / EAN: 9781441915757 Product Description: Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory,